淡江大學機構典藏:Item 987654321/92543
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    题名: A Comparative Study of Data Mining Techniques for Credit Scoring in Banking
    作者: Huang, Shih-Chen;Day, Min-Yuh
    贡献者: 淡江大學資訊管理學系
    关键词: Classification Method;Credit Risk Score;Data Mining;SAS Enterprise Miner;Support Vector Machine (SVM)
    日期: 2013-08-14
    上传时间: 2013-10-18 05:40:42 (UTC+8)
    出版者: IEEE Press
    摘要: Credit is becoming one of the most important incomes of banking. Past studies indicate that the credit risk scoring model has been better for Logistic Regression and Neural Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and reduce the credit risk. In this paper, we use data mining of enterprise software to construct four classification models, namely, decision tree, logistic regression, neural network and support vector machine, for credit scoring in banking. We conduct a systematic comparison and analysis on the accuracy of 17 classification models for credit scoring in banking. The contribution of this paper is that we use different classification methods to construct classification models and compare classification models accuracy, and the evidence demonstrates that the support vector machine models have higher accuracy rates and therefore outperform past classification methods in the context of credit scoring in banking.
    關聯: Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013), pp.684-691
    显示于类别:[資訊管理學系暨研究所] 會議論文

    文件中的档案:

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    2013_IEEE_IRI2013_A_Comparative_Study_of_Data_Mining_Techniques_for_Credit_Scoring_in_Banking_089_147.pdfA Comparative Study of Data Mining Techniques for Credit Scoring in Banking208KbAdobe PDF1029检视/开启
    The Analysis Approach of Voice of the Customers and Critical to Quality_英文摘要.docx摘要14KbMicrosoft Word199检视/开启

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